Ventricular mass, volumes and wall thickness at end diastole and end systole are essential clinical parameters for diagnosis and management of many cardiac diseases. Magnetic resonance imaging (MRI) may be used to estimate heart wall motion by reconstructing the shape and motion of the left ventricle.
MRI is also able to provide accurate and precise estimations of ventricular mass, volume and wall thickness, since it is a true 3-dimensional method which is not dependent on geometric assumptions and is not limited in the position or orientation of the possible images, unlike other methods, such as for example echocardiography or computed tomography.
Recent advances in MRI allow the acquisition of 10 to 20 MRI images or slices in short and long axis or arbitrary orientations, each with 10 to 25 frames through the cardiac cycle in real time, or ten to fifteen minutes or less, which is a clinically acceptable time. Previous studies have shown that the summation of areas outlined in short axis MRI slices gives more accurate and reproducible estimates of volume than echocardiography or LV angiography.
A major limitation of the MRI slice summation method is the prohibitive time required to outline the endocardial and epicardial boundaries of the left ventricle in each slice. This severely limits application of the use of the technique to routine clinical care.
In the past, many semi-automated image segmentation algorithms have been applied to this problem, but these solutions are frequently not sufficiently robust and accurate for routine clinical use. In particular the image pixel intensities are insufficient to adequately constrain the segmentation problem, due to the limited temporal and spatial resolution, presence of image artifacts, and lack of contrast between blood and muscle. The amount of time spent on manual editing and correction of contours obtained from these previous solutions renders automated methods nearly as slow as manual contouring in clinical practice.
Other techniques apply model fitting techniques to estimate characteristics of organs such as the left ventricle. T McInerney and D Terzopoulos in “A Dynamic Finite Element Surface Model for Segmentation and Tracking in Multidimensional Medical Images with Application to Cardiac 4D Image Analysis”, Computerized Medical Imaging and Graphics 19:69–83; 1995 describe a deformable “balloon” model that is topologically isomorphic to a sphere for use in estimating volume and motion of the left ventricle. PCT international patent publication WO 99/18450 to Philips AB titled “Method of and Device for Imaging an Object by means of Magnetic Resonance” describes the use of an ellipsoid to model the left ventricle. Both techniques require the use of edge detection algorithms and in doing so suffer from the disadvantages discussed above.